{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# KMeans 算法在使用 MKL 的 Windows 系统上存在内存泄漏问题\n",
    "# 设置下面的环境变量进行限制，避免问题\n",
    "os.environ['OMP_NUM_THREADS'] = \"1\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>种类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>0.2</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "    种类  \n",
       "0  0.0  \n",
       "1  0.0  \n",
       "2  0.0  \n",
       "3  0.0  \n",
       "4  0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取数据集\n",
    "dataset = pd.read_csv('data/鸢尾花.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>种类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)          种类  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe() # 观察数据，选择方差较大的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>种类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "   sepal length (cm)  petal length (cm)   种类\n",
       "0                5.1                1.4  0.0\n",
       "1                4.9                1.4  0.0\n",
       "2                4.7                1.3  0.0\n",
       "3                4.6                1.5  0.0\n",
       "4                5.0                1.4  0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择样本和标签，这里标签之后用来绘图，并不是参与训练\n",
    "sub_df = dataset[['sepal length (cm)', 'petal length (cm)', '种类']]\n",
    "X = sub_df.iloc[:,:-1].values # 需要聚类的数据样本\n",
    "y = sub_df.iloc[:,-1].values  # 用来绘图的标签\n",
    "sub_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "metadata": {},
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    "X"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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       "       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
       "       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据样本，使用散点图，并用标签设置散点的颜色\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3  # 设置簇的数量，后续会讨论为什么选择3个\n",
    "from sklearn.cluster import KMeans\n",
    "model = KMeans(n_clusters=k, n_init='auto', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, n_init='auto', random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在数据集上“拟合”：\n",
    "# 实际为 \n",
    "# 1、初始化参数\n",
    "# 2、迭代更新簇中心，直到满足终止条件。\n",
    "# 3、预测每个样本的簇标签\n",
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# “拟合”后，可以获得聚类结果，\n",
    "# 其中包括数据样本的簇和各个簇的质心\n",
    "clusters = model.labels_\n",
    "centroids = model.cluster_centers_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 0 2 0 0 0 0 2 0 0 0 0\n",
      " 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 2 0\n",
      " 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.83902439 5.67804878]\n",
      " [5.00784314 1.49215686]\n",
      " [5.87413793 4.39310345]]\n"
     ]
    }
   ],
   "source": [
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接着使用matplotlib，\n",
    "# 绘制“拟合”后的数据样本的簇标签和各个簇的质心\n",
    "# 设置多个子图模式，1行2列\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "# 第1个坐标轴，绘制样本散点图，并设置颜色为y\n",
    "ax[0].scatter(X[:, 0], X[:, 1], c=y)\n",
    "# 设置标题\n",
    "ax[0].set_title('actual')\n",
    "\n",
    "# 第2个坐标轴，绘制样本散点图，并设置颜色为聚类后的簇\n",
    "ax[1].scatter(X[:, 0], X[:, 1], c=clusters)\n",
    "# 同时绘制不同簇的质心，散点的形状为星星，颜色为红色\n",
    "ax[1].scatter(centroids[:, 0], centroids[:, 1],\n",
    "              marker='*', s=200, c='#FF0000', )\n",
    "# 设置标题\n",
    "ax[1].set_title('predict')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=1, SSE=23.801128824770757, inertia=566.4937333333332\n",
      "k=2, SSE=14.1086169493387, inertia=112.99207175925925\n",
      "k=3, SSE=12.511480301803205, inertia=53.80997864410694\n",
      "k=4, SSE=11.644919526369858, inertia=34.31702077922079\n",
      "k=5, SSE=11.157409296360004, inertia=25.802454427925\n",
      "k=6, SSE=11.272372264202028, inertia=22.72412034739455\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 设置不同的k值的列表\n",
    "k_list = list(range(1, 7))\n",
    "# 创建相同大小的列表，用来记录不同k值对应的SSE\n",
    "sse_list = [0] * len(k_list)\n",
    "inertia_list = [0] * len(k_list)\n",
    "\n",
    "# k的范围从1到6。然后，我们训练数据样本\n",
    "# 模型并分别记录生成的 SSE：\n",
    "\n",
    "for k_ind, k in enumerate(k_list):\n",
    "    # 进行聚类\n",
    "    model = KMeans(n_clusters=k, n_init='auto',random_state=42)\n",
    "    model.fit(X)\n",
    "    # 获取聚类后的簇标签\n",
    "    cluster = model.labels_\n",
    "    # 获取聚类后的不同簇的质心\n",
    "    centroids = model.cluster_centers_\n",
    "    \n",
    "    # 记录在当前k值下，所有簇的SSE之和\n",
    "    sse = 0\n",
    "    for i in range(k):\n",
    "        # 某一个簇的样本索引\n",
    "        cluster_i = np.where(cluster == i)\n",
    "        # 记录该簇的SSE，并累加到整体的SSE中\n",
    "        sse += np.linalg.norm(X[cluster_i] - centroids[i])\n",
    "    \n",
    "    \n",
    "    # 记录到存储SSE值的列表中\n",
    "    sse_list[k_ind] = sse\n",
    "    # 模型自带的inertia_\n",
    "    inertia_list[k_ind] = model.inertia_\n",
    "        # 输出\n",
    "    print('k={}, SSE={}, inertia={}'.format(k, sse, model.inertia_))\n",
    "\n",
    "# 绘制不同k值的SSE，观察拐点\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "ax[0].plot(k_list, sse_list, c='k', label='sse')\n",
    "ax[0].legend()\n",
    "ax[1].plot(k_list, inertia_list, c='r', label='inertia')\n",
    "ax[1].legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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